Subjects

Individual runs

First run

run1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run1_data)
##            term   estimate  std.error  statistic       p.value
## 1   (Intercept) -2.3535248 0.07394413 -31.828422 2.618309e-222
## 2        mask_c  0.1141508 0.10022360   1.138961  2.547195e-01
## 3        feat_c  0.3238523 0.10022514   3.231248  1.232507e-03
## 4 mask_c:feat_c  0.4674695 0.20044728   2.332132  1.969375e-02

Second run

run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run2_data)
##            term    estimate  std.error   statistic       p.value
## 1   (Intercept) -2.58289321 0.07710757 -33.4972722 5.281258e-246
## 2        mask_c  0.07250391 0.10332222   0.7017263  4.828499e-01
## 3        feat_c  0.35802662 0.10332944   3.4649044  5.304201e-04
## 4 mask_c:feat_c -0.19824320 0.20661826  -0.9594661  3.373240e-01

Third run

run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run3_data)
##            term     estimate  std.error    statistic       p.value
## 1   (Intercept) -2.620988407 0.07867492 -33.31415457 2.408629e-243
## 2        mask_c -0.004392269 0.08480700  -0.05179135  9.586949e-01
## 3        feat_c  0.295992799 0.08481076   3.49003844  4.829511e-04
## 4 mask_c:feat_c  0.019227262 0.16967720   0.11331671  9.097795e-01

Overall

feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                             family = binomial, data = question_first)
##            term    estimate  std.error   statistic      p.value
## 1   (Intercept) -2.53963369 0.04792303 -52.9940169 0.000000e+00
## 2        mask_c  0.05178888 0.05473196   0.9462273 3.440327e-01
## 3        feat_c  0.32306357 0.05473604   5.9022094 3.586655e-09
## 4 mask_c:feat_c  0.09328473 0.10947429   0.8521155 3.941500e-01